Resource Efficient Representation of Machine Learning Models : investigating optimization options for decision trees in embedded systems

Combining embedded systems and machine learning models is an exciting prospect. However, to fully target any embedded system, with the most stringent resource requirements, the models have to be designed with care not to overwhelm it. Decision tree ensembles are targeted in this thesis. A benchmark...

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Bibliographic details

Main Author:	Lundberg, Jacob
Format:	Publication
Language:	English
Place of publication:	Linköpings universitet, Statistik och maskininlärning 01.01.2019
Data of publication:	2019-01-01
Subjects:	Datorteknik rule fit lasso optimization embedded systems regression decision trees ensemble methods resources machine learning Computer Engineering
Online Access:	available in Bonn?
Database:	OpenAIRE (Open Access) OpenAIRE Database information Databases - DBIS